lightweight python library to query city/state name by zip code?

Try pyzipcode. An example from the home page:

>>> from pyzipcode import ZipCodeDatabase
>>> zcdb = ZipCodeDatabase()
>>> zipcode = zcdb[54115]
>>> zipcode.zip
u'54115'
>>> zipcode.city
u'De Pere'
>>> zipcode.state
u'WI'
>>> zipcode.longitude
-88.078959999999995
>>> zipcode.latitude
44.42042
>>> zipcode.timezone
-6

Use this library uszipcode.

Advantages:

• Data is up-to-date, super rich info, way richer and more up-to-date than zipcode and pyzipcode and any other python zipcode library.
• Query is super easy, and there are like 20+ built-in query pattern you can use. And you can customize your query anyway you want.
• Support fuzzy string match for city and state. You don't need to use the exact name.

>>> from uszipcode import ZipcodeSearchEngine
>>> search = ZipcodeSearchEngine()
>>> zipcode = search.by_zipcode("10001")
>>> print(zipcode)
{
    "City": "New York", 
    "Density": 34035.48387096774, 
    "HouseOfUnits": 12476, 
    "LandArea": 0.62, 
    "Latitude": 40.75368539999999, 
    "Longitude": -73.9991637, 
    "NEBoundLatitude": 40.8282129, 
    "NEBoundLongitude": -73.9321059, 
    "Population": 21102, 
    "SWBoundLatitude": 40.743451, 
    "SWBoungLongitude": -74.00794499999998, 
    "State": "NY", 
    "TotalWages": 1031960117.0, 
    "WaterArea": 0.0, 
    "Wealthy": 48903.42702113544, 
    "Zipcode": "10001", 
    "ZipcodeType": "Standard"
}

# fuzzy city, state search, case insensitive, spelling mistake tolerant
# all zipcode in new york
>>> result = search.by_city_and_state(city="newyork", state="NY")
>>> search.export_to_csv(result, "result.csv")

Very easy to use to build advance search

>>> result = search.find(city="new york", 
... wealthy=100000, sort_by="Wealthy", ascending=False, returns=10)

I built Zipcodes to remove the dependency on SQLite that all other zipcode libraries had. SQLite is not available in an AWS Lambda environment, so this library provides a lightweight, powerful querying interface over a gzipped JSON file containing U.S. zipcode data. Below are some examples:

Matching:

>>> # Handles of Zip+4 zip-codes nicely. :)
>>> pprint(zipcodes.matching('77429-1145'))
[{'zip_code': '77429',
  'zip_code_type': 'STANDARD',
  'city': 'CYPRESS',
  'state': 'TX',
  'lat': 29.96,
  'long': -95.69,
  'world_region': 'NA',
  'country': 'US',
  'active': True}]

Validity:

>>> # Whether the zip-code exists within the database.
>>> print(zipcodes.is_valid('06463'))
False

Similarity:

>>> # Search for zipcodes that begin with a pattern.
>>> pprint(zipcodes.similar_to('0643'))
[{'active': True,
  'city': 'GUILFORD',
  'country': 'US',
  'lat': 41.28,
  'long': -72.67,
  'state': 'CT',
  'world_region': 'NA',
  'zip_code': '06437',
  'zip_code_type': 'STANDARD'},
 {'active': True,
  'city': 'HADDAM',
  'country': 'US',
  'lat': 41.45,
  'long': -72.5,
  'state': 'CT',
  'world_region': 'NA',
  'zip_code': '06438',
  'zip_code_type': 'STANDARD'},
... # remaining results truncated for readability...
]

Advanced filtering:

>>> # Arbitrary nesting of similar_to and filter_by calls, allowing for great precision while filtering.
>>> pprint(zipcodes.similar_to('2', zips=zipcodes.filter_by(zipcodes.list_all(), active=True, city='WINDSOR')))
[{'active': True,
  'city': 'WINDSOR',
  'country': 'US',
  'lat': 33.48,
  'long': -81.51,
  'state': 'SC',
  'world_region': 'NA',
  'zip_code': '29856',
  'zip_code_type': 'STANDARD'},
 {'active': True,
  'city': 'WINDSOR',
  'country': 'US',
  'lat': 36.8,
  'long': -76.73,
  'state': 'VA',
  'world_region': 'NA',
  'zip_code': '23487',
  'zip_code_type': 'STANDARD'},
 {'active': True,
  'city': 'WINDSOR',
  'country': 'US',
  'lat': 36.0,
  'long': -76.94,
  'state': 'NC',
  'world_region': 'NA',
  'zip_code': '27983',
  'zip_code_type': 'STANDARD'}]